I am a visiting PhD student from Flinders University in Australia.
Approximately 30% of bacterial and 65% of viral protein sequences cannot be assigned a known biological function. To gain a better understanding of these microbes, my PhD research aims to develop new computational techniques and tools to decrease this ever-increasing sequence function gap. I use machine learning, sequence embeddings and computed protein structures combined with a range of genomic properties including gene arrangements to build software which allows researchers to annotate unknown microbial genes.
I have investigated how amino acid sequence embeddings may be used to study functional hierarchies for describing bacterial protein functions. Using a Protvec sequence embedding, a technique used to represent amino acid sequences in n-dimensional space, I demonstrated that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences. However, these sequences are currently described using 29 functional labels arranged within a hierarchy which is different to the hierarchical organisation of sequences within the Protvec embedding. Furthermore, by representing unknown sequences with Protvec, I demonstrated that unknown sequences form clusters that likely share related biological roles. Such clusters may be beneficial for selecting optimal candidate proteins to characterize experimentally.
Doi: https://doi.org/10.1186/s12859-022-04930-5External link
